Prediction of Protein Structure Using Surface Accessibility Data.

Christoph Hartlmüller,Tobias Madl,Christoph Göbl

doi:10.1002/anie.201604788

Abstract

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach.

Highlights

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound
Several groups have realized that the growing number of structural data available in the Protein Data Base[2] (PDB) provide a valuable source for NMR-based structure determination, in particular when combined with NMR chemical shifts.[3]
We describe an approach in which we exploit NMR-based surface accessibility data obtained from measurement of paramagnetic relaxation enhancements induced by a soluble paramagnetic compound for de novo structure prediction in the Rosetta framework.[6,7]

Summary

Introduction

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). We extended the Rosetta de novo structure prediction method to incorporate sPRE data to take advantage of the surface accessibility information in the folding of the protein backbone (Figure 1 c).

Results

Conclusion